Overview

Dataset statistics

Number of variables14
Number of observations569
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.4 KiB
Average record size in memory112.2 B

Variable types

NUM13
BOOL1

Reproduction

Analysis started2022-01-09 02:00:20.931480
Analysis finished2022-01-09 02:00:55.717885
Duration34.79 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

perimeter_mean is highly correlated with radius_mean and 4 other fieldsHigh correlation
radius_mean is highly correlated with perimeter_mean and 4 other fieldsHigh correlation
area_mean is highly correlated with radius_mean and 4 other fieldsHigh correlation
concave points_mean is highly correlated with concavity_mean and 1 other fieldsHigh correlation
concavity_mean is highly correlated with concave points_meanHigh correlation
perimeter_se is highly correlated with radius_se and 1 other fieldsHigh correlation
radius_se is highly correlated with perimeter_se and 1 other fieldsHigh correlation
area_se is highly correlated with radius_se and 1 other fieldsHigh correlation
radius_worst is highly correlated with radius_mean and 4 other fieldsHigh correlation
perimeter_worst is highly correlated with radius_mean and 4 other fieldsHigh correlation
area_worst is highly correlated with radius_mean and 4 other fieldsHigh correlation
concave points_worst is highly correlated with concave points_meanHigh correlation
concavity_mean has 13 (2.3%) zeros Zeros
concave points_mean has 13 (2.3%) zeros Zeros
concavity_worst has 13 (2.3%) zeros Zeros
concave points_worst has 13 (2.3%) zeros Zeros

Variables

diagnosis
Boolean

Distinct count2
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
0
357
1
212
ValueCountFrequency (%) 
035762.7%
 
121237.3%
 

radius_mean
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count456
Unique (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.127291739894552
Minimum6.981
Maximum28.11
Zeros0
Zeros (%)0.0%
Memory size4.6 KiB
2022-01-09T02:00:55.837534image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6.981
5-th percentile9.5292
Q111.7
median13.37
Q315.78
95-th percentile20.576
Maximum28.11
Range21.129
Interquartile range (IQR)4.08

Descriptive statistics

Standard deviation3.524048826
Coefficient of variation (CV)0.2494497099
Kurtosis0.8455216229
Mean14.12729174
Median Absolute Deviation (MAD)1.9
Skewness0.9423795717
Sum8038.429
Variance12.41892013
2022-01-09T02:00:55.968314image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
12.3440.7%
 
13.8530.5%
 
12.4630.5%
 
12.1830.5%
 
10.2630.5%
 
12.8930.5%
 
11.7130.5%
 
12.7730.5%
 
13.0530.5%
 
13.1730.5%
 
11.630.5%
 
11.8930.5%
 
11.0630.5%
 
15.4630.5%
 
1330.5%
 
11.8420.4%
 
12.8820.4%
 
20.1820.4%
 
11.3420.4%
 
13.8720.4%
 
13.6620.4%
 
12.0520.4%
 
11.7620.4%
 
10.5120.4%
 
13.220.4%
 
Other values (431)50388.4%
 
ValueCountFrequency (%) 
6.98110.2%
 
7.69110.2%
 
7.72910.2%
 
7.7610.2%
 
8.19610.2%
 
8.21910.2%
 
8.57110.2%
 
8.59710.2%
 
8.59810.2%
 
8.61810.2%
 
ValueCountFrequency (%) 
28.1110.2%
 
27.4210.2%
 
27.2210.2%
 
25.7310.2%
 
25.2210.2%
 
24.6310.2%
 
24.2510.2%
 
23.5110.2%
 
23.2910.2%
 
23.2710.2%
 

perimeter_mean
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count522
Unique (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.96903339191564
Minimum43.79
Maximum188.5
Zeros0
Zeros (%)0.0%
Memory size4.6 KiB
2022-01-09T02:00:56.103990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum43.79
5-th percentile60.496
Q175.17
median86.24
Q3104.1
95-th percentile135.82
Maximum188.5
Range144.71
Interquartile range (IQR)28.93

Descriptive statistics

Standard deviation24.29898104
Coefficient of variation (CV)0.2642082899
Kurtosis0.9722135477
Mean91.96903339
Median Absolute Deviation (MAD)12.71
Skewness0.9906504254
Sum52330.38
Variance590.4404795
2022-01-09T02:00:56.239233image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
87.7630.5%
 
134.730.5%
 
82.6130.5%
 
130.720.4%
 
123.720.4%
 
71.4920.4%
 
87.2120.4%
 
129.120.4%
 
117.420.4%
 
73.3420.4%
 
70.6720.4%
 
85.9820.4%
 
84.0820.4%
 
109.320.4%
 
88.3720.4%
 
132.920.4%
 
107.120.4%
 
94.2520.4%
 
78.8320.4%
 
74.7220.4%
 
103.720.4%
 
133.820.4%
 
97.2620.4%
 
58.7920.4%
 
152.120.4%
 
Other values (497)51690.7%
 
ValueCountFrequency (%) 
43.7910.2%
 
47.9210.2%
 
47.9810.2%
 
48.3410.2%
 
51.7110.2%
 
53.2710.2%
 
54.0910.2%
 
54.3410.2%
 
54.4210.2%
 
54.5310.2%
 
ValueCountFrequency (%) 
188.510.2%
 
186.910.2%
 
182.110.2%
 
174.210.2%
 
171.510.2%
 
166.210.2%
 
165.510.2%
 
158.910.2%
 
155.110.2%
 
153.510.2%
 

area_mean
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count539
Unique (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean654.8891036906855
Minimum143.5
Maximum2501.0
Zeros0
Zeros (%)0.0%
Memory size4.6 KiB
2022-01-09T02:00:56.375058image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum143.5
5-th percentile275.78
Q1420.3
median551.1
Q3782.7
95-th percentile1309.8
Maximum2501
Range2357.5
Interquartile range (IQR)362.4

Descriptive statistics

Standard deviation351.9141292
Coefficient of variation (CV)0.5373644594
Kurtosis3.652302762
Mean654.8891037
Median Absolute Deviation (MAD)153.3
Skewness1.645732176
Sum372631.9
Variance123843.5543
2022-01-09T02:00:56.483346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
512.230.5%
 
420.320.4%
 
684.520.4%
 
477.320.4%
 
466.120.4%
 
537.320.4%
 
361.620.4%
 
56120.4%
 
399.820.4%
 
126420.4%
 
52020.4%
 
394.120.4%
 
559.220.4%
 
107520.4%
 
758.620.4%
 
658.820.4%
 
514.320.4%
 
121420.4%
 
321.620.4%
 
641.220.4%
 
506.320.4%
 
43220.4%
 
107620.4%
 
113820.4%
 
372.720.4%
 
Other values (514)51891.0%
 
ValueCountFrequency (%) 
143.510.2%
 
170.410.2%
 
178.810.2%
 
18110.2%
 
201.910.2%
 
203.910.2%
 
221.210.2%
 
221.310.2%
 
221.810.2%
 
224.510.2%
 
ValueCountFrequency (%) 
250110.2%
 
249910.2%
 
225010.2%
 
201010.2%
 
187810.2%
 
184110.2%
 
176110.2%
 
174710.2%
 
168610.2%
 
168510.2%
 

concavity_mean
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count537
Unique (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0887993158172232
Minimum0.0
Maximum0.4268
Zeros13
Zeros (%)2.3%
Memory size4.6 KiB
2022-01-09T02:00:56.605553image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0049826
Q10.02956
median0.06154
Q30.1307
95-th percentile0.24302
Maximum0.4268
Range0.4268
Interquartile range (IQR)0.10114

Descriptive statistics

Standard deviation0.07971980871
Coefficient of variation (CV)0.8977525105
Kurtosis1.998637529
Mean0.08879931582
Median Absolute Deviation (MAD)0.04046
Skewness1.401179739
Sum50.5268107
Variance0.0063552479
2022-01-09T02:00:56.716230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0132.3%
 
0.120430.5%
 
0.197420.4%
 
0.100720.4%
 
0.111520.4%
 
0.0134220.4%
 
0.0197220.4%
 
0.244820.4%
 
0.241720.4%
 
0.0299520.4%
 
0.213320.4%
 
0.108520.4%
 
0.0589220.4%
 
0.0268820.4%
 
0.10120.4%
 
0.0334420.4%
 
0.0199720.4%
 
0.110320.4%
 
0.0842220.4%
 
0.0672620.4%
 
0.0420110.2%
 
0.00500610.2%
 
0.31310.2%
 
0.0112310.2%
 
0.0710710.2%
 
Other values (512)51290.0%
 
ValueCountFrequency (%) 
0132.3%
 
0.00069210.2%
 
0.000973710.2%
 
0.00119410.2%
 
0.00146110.2%
 
0.00148710.2%
 
0.00154610.2%
 
0.00159510.2%
 
0.00159710.2%
 
0.0018610.2%
 
ValueCountFrequency (%) 
0.426810.2%
 
0.426410.2%
 
0.410810.2%
 
0.375410.2%
 
0.363510.2%
 
0.352310.2%
 
0.351410.2%
 
0.336810.2%
 
0.333910.2%
 
0.320110.2%
 

concave points_mean
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count542
Unique (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04891914586994728
Minimum0.0
Maximum0.2012
Zeros13
Zeros (%)2.3%
Memory size4.6 KiB
2022-01-09T02:00:56.838284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0056208
Q10.02031
median0.0335
Q30.074
95-th percentile0.12574
Maximum0.2012
Range0.2012
Interquartile range (IQR)0.05369

Descriptive statistics

Standard deviation0.03880284486
Coefficient of variation (CV)0.7932036459
Kurtosis1.066555703
Mean0.04891914587
Median Absolute Deviation (MAD)0.02014
Skewness1.171180081
Sum27.834994
Variance0.001505660769
2022-01-09T02:00:56.970627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0132.3%
 
0.0286430.5%
 
0.0237720.4%
 
0.0203120.4%
 
0.147120.4%
 
0.0192420.4%
 
0.0646220.4%
 
0.0259420.4%
 
0.0227220.4%
 
0.124220.4%
 
0.0577820.4%
 
0.104320.4%
 
0.0236920.4%
 
0.0525220.4%
 
0.0161520.4%
 
0.0410710.2%
 
0.0140610.2%
 
0.0104310.2%
 
0.0121610.2%
 
0.0440810.2%
 
0.00793710.2%
 
0.0996110.2%
 
0.0703810.2%
 
0.0287710.2%
 
0.0136410.2%
 
Other values (517)51790.9%
 
ValueCountFrequency (%) 
0132.3%
 
0.00185210.2%
 
0.00240410.2%
 
0.00292410.2%
 
0.00294110.2%
 
0.00312510.2%
 
0.00326110.2%
 
0.00333310.2%
 
0.00347210.2%
 
0.00416710.2%
 
ValueCountFrequency (%) 
0.201210.2%
 
0.191310.2%
 
0.187810.2%
 
0.184510.2%
 
0.182310.2%
 
0.168910.2%
 
0.16210.2%
 
0.160410.2%
 
0.159510.2%
 
0.156210.2%
 

radius_se
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count540
Unique (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40517205623901575
Minimum0.1115
Maximum2.873
Zeros0
Zeros (%)0.0%
Memory size4.6 KiB
2022-01-09T02:00:57.111063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.1115
5-th percentile0.1601
Q10.2324
median0.3242
Q30.4789
95-th percentile0.95952
Maximum2.873
Range2.7615
Interquartile range (IQR)0.2465

Descriptive statistics

Standard deviation0.277312733
Coefficient of variation (CV)0.6844320301
Kurtosis17.68672597
Mean0.4051720562
Median Absolute Deviation (MAD)0.106
Skewness3.088612166
Sum230.5429
Variance0.07690235188
2022-01-09T02:00:57.229815image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.220430.5%
 
0.28630.5%
 
0.507920.4%
 
0.297620.4%
 
0.18420.4%
 
0.16320.4%
 
0.40320.4%
 
0.410120.4%
 
0.327620.4%
 
0.257720.4%
 
0.160120.4%
 
0.262120.4%
 
0.192420.4%
 
0.223920.4%
 
0.33820.4%
 
0.595920.4%
 
0.231520.4%
 
0.353420.4%
 
0.235120.4%
 
0.295720.4%
 
0.153220.4%
 
0.30620.4%
 
0.256220.4%
 
0.24320.4%
 
0.268420.4%
 
Other values (515)51790.9%
 
ValueCountFrequency (%) 
0.111510.2%
 
0.114410.2%
 
0.115310.2%
 
0.116610.2%
 
0.118610.2%
 
0.119410.2%
 
0.119910.2%
 
0.12110.2%
 
0.126710.2%
 
0.130210.2%
 
ValueCountFrequency (%) 
2.87310.2%
 
2.54710.2%
 
1.50910.2%
 
1.3710.2%
 
1.29610.2%
 
1.29210.2%
 
1.29110.2%
 
1.21510.2%
 
1.21410.2%
 
1.20710.2%
 

perimeter_se
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count533
Unique (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8660592267135327
Minimum0.757
Maximum21.98
Zeros0
Zeros (%)0.0%
Memory size4.6 KiB
2022-01-09T02:00:57.363669image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.757
5-th percentile1.1328
Q11.606
median2.287
Q33.357
95-th percentile7.0416
Maximum21.98
Range21.223
Interquartile range (IQR)1.751

Descriptive statistics

Standard deviation2.021854554
Coefficient of variation (CV)0.7054475829
Kurtosis21.40190493
Mean2.866059227
Median Absolute Deviation (MAD)0.77
Skewness3.443615202
Sum1630.7877
Variance4.087895838
2022-01-09T02:00:57.479846image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.77840.7%
 
2.22520.4%
 
1.53520.4%
 
2.90320.4%
 
2.40620.4%
 
2.36320.4%
 
2.09720.4%
 
1.49120.4%
 
1.47120.4%
 
1.59720.4%
 
3.76720.4%
 
1.56620.4%
 
3.56420.4%
 
2.76520.4%
 
2.15520.4%
 
1.44520.4%
 
1.10120.4%
 
1.14320.4%
 
1.66720.4%
 
3.00820.4%
 
1.95920.4%
 
2.87320.4%
 
2.56920.4%
 
2.2320.4%
 
2.74720.4%
 
Other values (508)51790.9%
 
ValueCountFrequency (%) 
0.75710.2%
 
0.771410.2%
 
0.843910.2%
 
0.848410.2%
 
0.87310.2%
 
0.921910.2%
 
0.96810.2%
 
0.981210.2%
 
0.985710.2%
 
0.988710.2%
 
ValueCountFrequency (%) 
21.9810.2%
 
18.6510.2%
 
11.0710.2%
 
10.1210.2%
 
10.0510.2%
 
9.80710.2%
 
9.63510.2%
 
9.42410.2%
 
8.86710.2%
 
8.8310.2%
 

area_se
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count528
Unique (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.337079086116
Minimum6.802
Maximum542.2
Zeros0
Zeros (%)0.0%
Memory size4.6 KiB
2022-01-09T02:00:57.603563image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6.802
5-th percentile11.36
Q117.85
median24.53
Q345.19
95-th percentile115.8
Maximum542.2
Range535.398
Interquartile range (IQR)27.34

Descriptive statistics

Standard deviation45.49100552
Coefficient of variation (CV)1.127771434
Kurtosis49.20907651
Mean40.33707909
Median Absolute Deviation (MAD)9.19
Skewness5.447186285
Sum22951.798
Variance2069.431583
2022-01-09T02:00:57.716216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
18.5430.5%
 
16.9730.5%
 
17.6730.5%
 
16.6430.5%
 
34.3720.4%
 
19.8720.4%
 
20.7420.4%
 
12.6720.4%
 
15.2420.4%
 
22.7920.4%
 
44.4120.4%
 
23.9220.4%
 
20.9820.4%
 
15.7520.4%
 
16.3920.4%
 
18.1520.4%
 
11.2820.4%
 
19.5320.4%
 
17.7420.4%
 
11.3620.4%
 
17.8520.4%
 
22.2220.4%
 
17.8620.4%
 
20.220.4%
 
33.0120.4%
 
Other values (503)51590.5%
 
ValueCountFrequency (%) 
6.80210.2%
 
7.22810.2%
 
7.25410.2%
 
7.32610.2%
 
8.20510.2%
 
8.32210.2%
 
8.60510.2%
 
8.95510.2%
 
8.96610.2%
 
9.00610.2%
 
ValueCountFrequency (%) 
542.210.2%
 
525.610.2%
 
23310.2%
 
224.110.2%
 
199.710.2%
 
180.210.2%
 
176.510.2%
 
17010.2%
 
164.110.2%
 
158.710.2%
 

radius_worst
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count457
Unique (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.269189806678387
Minimum7.93
Maximum36.04
Zeros0
Zeros (%)0.0%
Memory size4.6 KiB
2022-01-09T02:00:57.839095image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7.93
5-th percentile10.534
Q113.01
median14.97
Q318.79
95-th percentile25.64
Maximum36.04
Range28.11
Interquartile range (IQR)5.78

Descriptive statistics

Standard deviation4.83324158
Coefficient of variation (CV)0.2970794267
Kurtosis0.9440895759
Mean16.26918981
Median Absolute Deviation (MAD)2.46
Skewness1.103115206
Sum9257.169
Variance23.36022418
2022-01-09T02:00:57.962888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
12.3650.9%
 
13.540.7%
 
13.3440.7%
 
15.5330.5%
 
19.8530.5%
 
13.7530.5%
 
12.430.5%
 
16.7630.5%
 
12.8430.5%
 
16.1130.5%
 
13.3530.5%
 
13.7430.5%
 
15.1130.5%
 
11.9230.5%
 
12.9830.5%
 
15.0530.5%
 
14.9130.5%
 
13.4530.5%
 
14.830.5%
 
16.4130.5%
 
13.0630.5%
 
16.4630.5%
 
15.1430.5%
 
14.2420.4%
 
14.220.4%
 
Other values (432)49286.5%
 
ValueCountFrequency (%) 
7.9310.2%
 
8.67810.2%
 
8.95210.2%
 
8.96410.2%
 
9.07710.2%
 
9.09210.2%
 
9.26210.2%
 
9.41410.2%
 
9.45610.2%
 
9.47310.2%
 
ValueCountFrequency (%) 
36.0410.2%
 
33.1310.2%
 
33.1210.2%
 
32.4910.2%
 
31.0110.2%
 
30.7910.2%
 
30.7510.2%
 
30.6710.2%
 
3010.2%
 
29.9210.2%
 

perimeter_worst
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count514
Unique (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.26121265377857
Minimum50.41
Maximum251.2
Zeros0
Zeros (%)0.0%
Memory size4.6 KiB
2022-01-09T02:00:58.092541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum50.41
5-th percentile67.856
Q184.11
median97.66
Q3125.4
95-th percentile171.64
Maximum251.2
Range200.79
Interquartile range (IQR)41.29

Descriptive statistics

Standard deviation33.60254227
Coefficient of variation (CV)0.3132776652
Kurtosis1.070149667
Mean107.2612127
Median Absolute Deviation (MAD)16.87
Skewness1.128163871
Sum61031.63
Variance1129.130847
2022-01-09T02:00:58.218772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
117.730.5%
 
101.730.5%
 
105.930.5%
 
11220.4%
 
85.0720.4%
 
113.820.4%
 
170.320.4%
 
76.5120.4%
 
84.5320.4%
 
101.220.4%
 
160.520.4%
 
78.2720.4%
 
12920.4%
 
100.920.4%
 
87.3620.4%
 
115.920.4%
 
129.320.4%
 
108.620.4%
 
79.7320.4%
 
102.520.4%
 
184.620.4%
 
113.120.4%
 
87.2220.4%
 
123.520.4%
 
92.0420.4%
 
Other values (489)51690.7%
 
ValueCountFrequency (%) 
50.4110.2%
 
54.4910.2%
 
56.6510.2%
 
57.1710.2%
 
57.2610.2%
 
58.0810.2%
 
58.3610.2%
 
59.1610.2%
 
59.910.2%
 
60.910.2%
 
ValueCountFrequency (%) 
251.210.2%
 
229.310.2%
 
220.810.2%
 
21410.2%
 
211.710.2%
 
211.510.2%
 
206.810.2%
 
20610.2%
 
205.710.2%
 
202.410.2%
 

area_worst
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count544
Unique (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean880.5831282952548
Minimum185.2
Maximum4254.0
Zeros0
Zeros (%)0.0%
Memory size4.6 KiB
2022-01-09T02:00:58.360862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum185.2
5-th percentile331.06
Q1515.3
median686.5
Q31084
95-th percentile2009.6
Maximum4254
Range4068.8
Interquartile range (IQR)568.7

Descriptive statistics

Standard deviation569.3569927
Coefficient of variation (CV)0.6465681369
Kurtosis4.396394829
Mean880.5831283
Median Absolute Deviation (MAD)215.6
Skewness1.859373272
Sum501051.8
Variance324167.3851
2022-01-09T02:00:58.481819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
708.820.4%
 
698.820.4%
 
439.620.4%
 
70620.4%
 
45820.4%
 
284.420.4%
 
808.920.4%
 
546.720.4%
 
126120.4%
 
547.420.4%
 
830.520.4%
 
624.120.4%
 
489.520.4%
 
121020.4%
 
143720.4%
 
733.520.4%
 
162320.4%
 
402.820.4%
 
725.920.4%
 
749.920.4%
 
175020.4%
 
472.420.4%
 
160320.4%
 
126920.4%
 
826.420.4%
 
Other values (519)51991.2%
 
ValueCountFrequency (%) 
185.210.2%
 
223.610.2%
 
240.110.2%
 
242.210.2%
 
24810.2%
 
249.810.2%
 
259.210.2%
 
268.610.2%
 
27010.2%
 
273.910.2%
 
ValueCountFrequency (%) 
425410.2%
 
343210.2%
 
323410.2%
 
321610.2%
 
314310.2%
 
294410.2%
 
290610.2%
 
278210.2%
 
264210.2%
 
261510.2%
 

concavity_worst
Real number (ℝ≥0)

ZEROS

Distinct count539
Unique (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27218848330404216
Minimum0.0
Maximum1.252
Zeros13
Zeros (%)2.3%
Memory size4.6 KiB
2022-01-09T02:00:58.616212image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01836
Q10.1145
median0.2267
Q30.3829
95-th percentile0.68238
Maximum1.252
Range1.252
Interquartile range (IQR)0.2684

Descriptive statistics

Standard deviation0.2086242806
Coefficient of variation (CV)0.7664699038
Kurtosis1.615253298
Mean0.2721884833
Median Absolute Deviation (MAD)0.132
Skewness1.150236822
Sum154.875247
Variance0.04352409046
2022-01-09T02:00:58.742234image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0132.3%
 
0.137730.5%
 
0.450430.5%
 
0.286620.4%
 
0.385320.4%
 
0.334420.4%
 
0.36320.4%
 
0.142320.4%
 
0.180420.4%
 
0.25620.4%
 
0.179120.4%
 
0.229820.4%
 
0.181120.4%
 
0.264420.4%
 
0.396520.4%
 
0.156420.4%
 
0.402420.4%
 
0.387910.2%
 
0.0404310.2%
 
0.22110.2%
 
0.555310.2%
 
0.0253310.2%
 
0.659910.2%
 
0.226710.2%
 
0.434110.2%
 
Other values (514)51490.3%
 
ValueCountFrequency (%) 
0132.3%
 
0.00184510.2%
 
0.00358110.2%
 
0.00495510.2%
 
0.00551810.2%
 
0.00557910.2%
 
0.0069210.2%
 
0.00773210.2%
 
0.00797710.2%
 
0.0100510.2%
 
ValueCountFrequency (%) 
1.25210.2%
 
1.1710.2%
 
1.10510.2%
 
0.960810.2%
 
0.938710.2%
 
0.903410.2%
 
0.901910.2%
 
0.848910.2%
 
0.848810.2%
 
0.840210.2%
 

concave points_worst
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count492
Unique (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11460622319859401
Minimum0.0
Maximum0.291
Zeros13
Zeros (%)2.3%
Memory size4.6 KiB
2022-01-09T02:00:58.875846image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.024286
Q10.06493
median0.09993
Q30.1614
95-th percentile0.23692
Maximum0.291
Range0.291
Interquartile range (IQR)0.09647

Descriptive statistics

Standard deviation0.0657323412
Coefficient of variation (CV)0.5735494929
Kurtosis-0.5355351225
Mean0.1146062232
Median Absolute Deviation (MAD)0.04457
Skewness0.4926155269
Sum65.210941
Variance0.004320740679
2022-01-09T02:00:58.995789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0132.3%
 
0.0629630.5%
 
0.0743130.5%
 
0.170830.5%
 
0.0430630.5%
 
0.182730.5%
 
0.0555630.5%
 
0.121830.5%
 
0.0256430.5%
 
0.110530.5%
 
0.140720.4%
 
0.115520.4%
 
0.173220.4%
 
0.0792620.4%
 
0.107520.4%
 
0.161320.4%
 
0.0823520.4%
 
0.0384620.4%
 
0.0652820.4%
 
0.184120.4%
 
0.091420.4%
 
0.058920.4%
 
0.0481520.4%
 
0.137420.4%
 
0.247520.4%
 
Other values (467)49987.7%
 
ValueCountFrequency (%) 
0132.3%
 
0.00877210.2%
 
0.00925910.2%
 
0.0104210.2%
 
0.0111120.4%
 
0.0138910.2%
 
0.0163510.2%
 
0.0166710.2%
 
0.0185210.2%
 
0.0202210.2%
 
ValueCountFrequency (%) 
0.29110.2%
 
0.290310.2%
 
0.286710.2%
 
0.275610.2%
 
0.273310.2%
 
0.270110.2%
 
0.268810.2%
 
0.268510.2%
 
0.265410.2%
 
0.26510.2%
 

Interactions

2022-01-09T02:00:25.390156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:25.543129image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:25.699127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:25.852746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:26.004517image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:26.176306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:26.335806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:26.483870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:26.635370image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:26.782639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:26.946007image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:27.104657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:27.256234image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:27.409628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:27.569629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:27.745783image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:27.910402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:28.077808image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:28.263447image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:28.437384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:28.600962image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:28.768386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:28.938676image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:29.130409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:29.497952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:29.672663image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:29.851672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:29.997066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:30.159075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:30.308142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:30.458549image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:30.626890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:30.791063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:30.953881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:31.118015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:31.281554image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:31.469857image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:31.650442image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:31.831468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:31.991615image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:32.140358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:32.301897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:32.451776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:32.601541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:32.773170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:32.933121image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:33.084566image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:33.237027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:33.389969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:33.556244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:33.723508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:33.905399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:34.069821image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:34.241277image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:34.428177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:34.604152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:34.781437image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:34.976197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:35.159413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:35.333087image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:35.509960image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:35.687206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:35.878217image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:36.073025image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:36.457579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:36.643921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:36.802416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:36.981927image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:37.149752image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:37.312084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:37.492949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:37.661152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:37.824124image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:37.989253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:38.153684image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:38.331557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:38.509238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:38.680476image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:38.860859image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:39.007464image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:39.167511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:39.314510image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:39.463223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:39.629432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:39.786508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:39.941123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:40.092260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:40.242758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:40.406081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:40.569389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:40.726863image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:40.891769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:41.043830image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:41.209432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:41.362255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:41.515440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:41.689637image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:41.853815image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:42.006438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:42.169089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:42.331682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:42.501228image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:42.670453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:42.832774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:42.998403image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:43.148803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:43.319509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:43.483266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:43.643568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:43.820239image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:43.984089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:44.136676image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:00:44.291776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-09T02:00:59.480707image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-09T02:01:00.086201image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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Sample

First rows

diagnosisradius_meanperimeter_meanarea_meanconcavity_meanconcave points_meanradius_seperimeter_searea_seradius_worstperimeter_worstarea_worstconcavity_worstconcave points_worst
0117.99122.801001.00.300100.147101.09508.589153.4025.38184.602019.00.71190.2654
1120.57132.901326.00.086900.070170.54353.39874.0824.99158.801956.00.24160.1860
2119.69130.001203.00.197400.127900.74564.58594.0323.57152.501709.00.45040.2430
3111.4277.58386.10.241400.105200.49563.44527.2314.9198.87567.70.68690.2575
4120.29135.101297.00.198000.104300.75725.43894.4422.54152.201575.00.40000.1625
5112.4582.57477.10.157800.080890.33452.21727.1915.47103.40741.60.53550.1741
6118.25119.601040.00.112700.074000.44673.18053.9122.88153.201606.00.37840.1932
7113.7190.20577.90.093660.059850.58353.85650.9617.06110.60897.00.26780.1556
8113.0087.50519.80.185900.093530.30632.40624.3215.49106.20739.30.53900.2060
9112.4683.97475.90.227300.085430.29762.03923.9415.0997.65711.41.10500.2210

Last rows

diagnosisradius_meanperimeter_meanarea_meanconcavity_meanconcave points_meanradius_seperimeter_searea_seradius_worstperimeter_worstarea_worstconcavity_worstconcave points_worst
559011.5174.52403.50.111200.041050.23881.93616.9712.48082.28474.20.36300.09653
560014.0591.38600.40.044620.043040.36452.88829.8415.300100.20706.70.13260.10480
561011.2070.67386.00.000000.000000.31412.04122.8111.92075.19439.60.00000.00000
562115.22103.40716.90.255000.094290.26022.36222.6517.520128.70915.01.17000.23560
563120.92143.001347.00.317400.147400.96228.758118.8024.290179.101819.00.65990.25420
564121.56142.001479.00.243900.138901.17607.673158.7025.450166.102027.00.41070.22160
565120.13131.201261.00.144000.097910.76555.20399.0423.690155.001731.00.32150.16280
566116.60108.30858.10.092510.053020.45643.42548.5518.980126.701124.00.34030.14180
567120.60140.101265.00.351400.152000.72605.77286.2225.740184.601821.00.93870.26500
56807.7647.92181.00.000000.000000.38572.54819.159.45659.16268.60.00000.00000